3rd Workshop on Formal Verification and Machine Learning (WFVML 2024)

[Proposal] Co-located with TBD conference

Date: TBD (Full-Day)

Location: TBD (Physical Workshop)

[Proposal] About This Workshop

For safety-critical domains, the high performance of machine learning (ML) systems is not enough to ensure reliable operation: applications such as autonomous driving, human-robot interaction, and medical imaging require rigorous safety guarantees. On one hand, ML techniques are scalable and accurate, but struggle to provide strong guarantees for safety-critical applications. On the other hand, formal verification methods come with rigorous guarantees but suffer from scalability issues. Encouraging dialogue between these two research communities is, therefore, crucial to their mutual advancement and success. The integration of ML and formal verification is a young and interdisciplinary field that sits at the intersection of machine learning, robotics, programming languages, and security, among others, with a key aim at simultaneously achieving accuracy, scalability, and provable safety for data-driven computational systems.

The aims of this workshop are:

Our workshop features a diverse panel of invited speakers spanning research backgrounds from formal methods and programming languages to robust machine learning and applied AI. Please check out our tentative workshop schedule.

Workshop Organizers

University of Pennsylvania

University of Tübingen

ETH Zurich

Important Dates